Create Matching Config

Create a matching configuration with field mappings, match types (exact, fuzzy_string, date_range, numeric_range), and per-field weights that sum to 1.0.

Create a matching configuration that defines how documents are compared against a reference dataset. Each field mapping specifies an extracted_field (from extracted documents), a reference_field (a column in the reference data), a match_type, and a relative weight.

The typical workflow is: upload reference data via POST /v1/matching/reference-data, create a config with field mappings, then trigger a run via POST /v1/matching/configs/:id/run. For complex datasets, use POST /v1/matching/strategies/generate first to get AI-recommended mappings and weights.

The response returns the config with the saved field_mappings, threshold (defaults to 0.85), and links.runs URL for triggering runs. The reference_data_id is fixed at creation — to match against a different dataset, create a new config.

Choose match types carefully: use exact for standardized codes and IDs, fuzzy_string for names with potential typos, date_range for dates with tolerance, and numeric_range for amounts with rounding differences. Weights must sum to 1.0 — fields with higher weights have more influence on the overall confidence score.

• exact — case-insensitive string equality. Best for codes, IDs, and standardized values.
• fuzzy_string — Levenshtein similarity with a per-mapping threshold (default 0.8). Handles name variations and minor typos.
• date_range — date proximity within a configurable tolerance_days window.
• numeric_range — numeric proximity within a configurable tolerance_pct. Handles rounding differences.
• best_of — scores several alternatives (each its own field pair and match type) and keeps the best. sequence compares list fields element-wise via sequence_config.

{
  "name": "Vendor Invoice Match",
  "reference_data_id": "b2c3d4e5-f6a7-8901-bcde-f12345678901",
  "field_mappings": [
    { "extracted_field": "vendor_name", "reference_field": "name", "match_type": "fuzzy_string", "weight": 0.4 },
    { "extracted_field": "invoice_date", "reference_field": "date", "match_type": "date_range", "tolerance_days": 5, "weight": 0.3 },
    { "extracted_field": "amount", "reference_field": "total", "match_type": "numeric_range", "tolerance_pct": 1, "weight": 0.3 }
  ],
  "threshold": 0.85
}

Response

{
  "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "name": "Vendor Invoice Match",
  "reference_data_id": "b2c3d4e5-f6a7-8901-bcde-f12345678901",
  "target_type": "run",
  "target_value": {},
  "field_mappings": [
    { "extracted_field": "vendor_name", "reference_field": "name", "match_type": "fuzzy_string", "weight": 0.4 },
    { "extracted_field": "invoice_date", "reference_field": "date", "match_type": "date_range", "tolerance_days": 5, "weight": 0.3 },
    { "extracted_field": "amount", "reference_field": "total", "match_type": "numeric_range", "tolerance_pct": 1, "weight": 0.3 }
  ],
  "threshold": 0.85,
  "created_at": "2024-10-01T08:00:00.000Z",
  "updated_at": "2024-10-01T08:00:00.000Z",
  "links": {
    "self": "/v1/matching/configs/a1b2c3d4-e5f6-7890-abcd-ef1234567890",
    "runs": "/v1/matching/configs/a1b2c3d4-e5f6-7890-abcd-ef1234567890/run"
  }
}

Errors